Helicobacter pylori is one of the most widespread infections, and it is reaching alarming resistance levels worldwide. The recommended first-line empirical treatment differs according to the local rate of clarithromycin resistance. Macrolide resistance is mainly associated with three point mutations in the 23S rRNA gene. The aim of this study was to describe the antibiotic susceptibility of H. pylori in our healthcare area and the main mechanisms involved in clarithromycin resistance. Gastric biopsies (n = 641) were collected and cultured in a one-year prospective study. Antibiotic susceptibility testing was performed by gradient diffusion. A multiplex real-time PCR test (AllplexTM H.pylori & ClariR Assay, Seegene) was used to detect the most frequent mutations associated with clarithromycin resistance. Overall, 141 isolates were available for antibiotic susceptibility testing. The highest resistance rates were detected in metronidazole and levofloxacin. The rate of clarithromycin resistance was 12.1%, and the associated mutations were A2143G and A2142G. More than half of the clarithromycin-resistant isolates presented high MIC values (>256 mg/L). Tetracycline resistance was not detected, suggesting that therapies that contain tetracycline could be a suitable option. The low clarithromycin resistance rate coupled with the high rates of metronidazole resistance may support the recovery of the classical triple therapy in our healthcare area.

were combined to produce the risk level for each situation. Only 2 Delphi rounds were necessary. Results After the first round a consensus was reached for 8 situations. Experts agreed on the level of risk associated with 48 out of 52 modelled situations. A high or extreme consensus risk level is determined for 45 modelled situations. These situations represent a variety of drug-related problems. Overdosing was the most frequent situation [12 (22%)]. Cardiovascular, Psychiatric and Endocrinological drug classes are the most common involved in respectively [25 (45%)], [7 (13%)] and [5 (9%)] situations. Conclusion and RelevanceThe symbolic artificial intelligence to detect drug-related problems in patients' medications will be much more shared if pharmaceutical algorithms including the clinical risk are defined through consensus.

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but that there were isolated cases described in the bibliography. No response was obtained for two drugs. Conclusion and relevanceThe information collected in the EPAR is insufficient to resolve doubts for an oncohaematological pharmaceutical care consultation in most cases and it is necessary to perform bibliographic searches to obtain quality information. Laboratories should provide all available information and implement trials to evaluate the efficacy of drugs in real practice.